Fault diagnosis based on Danger Model Immune wavelet neural network

Author

Zhang, Chuang ; GUO, Chen ; Xu, Qingyang

Author_Institution

Coll. of Inf. Sci. & Technol., Dalian Maritime Univ., Dalian, China

fYear

2010

fDate

13-15 Aug. 2010

Firstpage

217

Lastpage

221

Abstract

Danger Model Immune Algorithm (DMIA) is an algorithm based on the danger theory of biological immune system, and it has a good performance in optimization. DMIA is proposed to initialize the weights and biases of wavelet neural network (WNN), the ergodic weights and biases are used for further net-training. The fault diagnosis for marine diesel engine is conducted by using the well-trained wavelet network. The results indicate that this algorithm is efficient in fault diagnosis.

Keywords

biology computing; diesel engines; fault diagnosis; genetic engineering; living systems; neural nets; optimisation; wavelet transforms; biological immune system; danger model immune wavelet neural network; danger theory; ergodic weights; fault diagnosis; marine diesel engine; net-training; optimization; Accuracy; Artificial neural networks; Diesel engines; Fault diagnosis; Immune system; Optimization; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Information Processing (ICICIP), 2010 International Conference on

Conference_Location

Dalian

Print_ISBN

978-1-4244-7047-1

Type

conf

DOI

10.1109/ICICIP.2010.5565265

Filename

5565265

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1962580